Effect of the Butyrate Prodrug Pivaloyloxymethyl Butyrate (AN9) on a Mouse Model for Spinal Muscular Atrophy.

Spinal muscular atrophy (SMA) is an early-onset motor neuron disease that leads to loss of muscle function. Butyrate (BA)-based compounds markedly improve the survival and motor phenotype of SMA mice. In this study, we examine the protective effects of the BA prodrug pivaloyloxymethyl butyrate (AN9) on the survival of SMNΔ7 SMA mice. Oral administration of AN9 beginning at PND04 almost doubled the average lifespan of SMNΔ7 SMA mice. AN9 treatment also increased the growth rate of SMNΔ7 SMA mice when compared to vehicle-treated SMNΔ7 SMA mice. In conclusion, BA prodrugs like AN9 have ameliorative effects on SMNΔ7 SMA mice

Dynamical moment of inertia and quadrupole vibrations in rotating nuclei

The contribution of quantum shape fluctuations to inertial properties of rotating nuclei has been analysed within the self-consistent one-dimensional cranking oscillator model. It is shown that in even-even nuclei the dynamical moment of inertia calculated in the mean field approximation is equivalent to the Thouless-Valatin moment of inertia calculated in the random phase approximation if and only if the self-consistent conditions for the mean field are fulfilled.Comment: 4 pages, 2 figure

Factors influencing the emigration of juvenile Bonga from the Cross River estuary

Studies were conducted to identify and quantify the proximate factors responsible for the emigration of juvenile bonga Ethmalosa fimbriata (Bowdich, 1825) from the Cross River estuary. A time series of bonga cpue, salinity, turbidity and plankton abundance was undertaken, juvenile bonga was abundant in the estuary when salinities ranged between 1 and 9ppt. at salinities outside this range, they were absent. We conclude that salinity is the proximate factor that initiates the emigration of juvenile bonga from the estuar

Enhancement of Sandwich Algorithms for Approximating Higher Dimensional Convex Pareto Sets

In many fields, we come across problems where we want to optimize several conflicting objectives simultaneously. To find a good solution for such multi-objective optimization problems, an approximation of the Pareto set is often generated. In this paper, we con- sider the approximation of Pareto sets for problems with three or more convex objectives and with convex constraints. For these problems, sandwich algorithms can be used to de- termine an inner and outer approximation between which the Pareto set is 'sandwiched'. Using these two approximations, we can calculate an upper bound on the approximation error. This upper bound can be used to determine which parts of the approximations must be improved and to provide a quality guarantee to the decision maker. In this paper, we extend higher dimensional sandwich algorithms in three different ways. Firstly, we introduce the new concept of adding dummy points to the inner approx- imation of a Pareto set. By using these dummy points, we can determine accurate inner and outer approximations more e±ciently, i.e., using less time-consuming optimizations. Secondly, we introduce a new method for the calculation of an error measure which is easy to interpret. The combination of easy calculation and easy interpretation makes this measure very suitable for sandwich algorithms. Thirdly, we show how transforming cer- tain objective functions can improve the results of sandwich algorithms and extend their applicability to certain non-convex problems. The calculation of the introduced error measure when using transformations will also be discussed. To show the effect of these enhancements, we make a numerical comparison using four test cases, including a four-dimensional case from the field of intensity-modulated radiation therapy (IMRT). The results of the different cases show that we can indeed achieve an accurate approximation using significantly fewer optimizations by using the enhancements.Convexity;e-efficiency;e-Pareto optimality;Geometric programming;Higher dimensional;Inner and outer approximation;IMRT;Pareto set;Multi-objective optimiza- tion;Sandwich algorithms;Transformations

One-Dimensional Nested Maximin Designs

The design of computer experiments is an important step in black box evaluation and optimization processes.When dealing with multiple black box functions the need often arises to construct designs for all black boxes jointly, instead of individually.These so-called nested designs are used to deal with linking parameters and sequential evaluations.In this paper we discuss one-dimensional nested maximin designs.We show how to nest two designs optimally and develop a heuristic to nest three and four designs.Furthermore, it is proven that the loss in space-fillingness, with respect to traditional maximin designs, is at most 14:64 percent and 19:21 percent, when nesting two and three designs, respectively.simulation;computers;integer programming